Our MapScore paper is now in press at Transactions in GIS! From the abstract:

The MapScore project described here provides a way to evaluate probability maps using actual historical searches. In this work we generated probability maps based on the statistical Euclidean distance tables from ISRID data (Koester, 2008), and compared them to Doke’s (2012) watershed model. Watershed boundaries follow high terrain and may better reflect actual barriers to travel. We also created a third model using the joint distribution using Euclidean and watershed features. On a metric where random maps score 0 and perfect maps score 1, the ISRID Distance Ring model scored 0.78 (95%CI: 0.74-0.82, on 376 cases). The simple Watershed model by itself was clearly inferior at 0.61, but the Combined model was slightly better at .81 (95%CI: 0.77-0.84).

A logical extension of the Distance Rings model is to fit a smooth function to the distribution of data found in ISRID. Examining the Euclidean Distance data for different categories, it was found that a lognormal curve roughly captured the shape of the data. The Log-Normal (LN) is a two parameter distribution which assumes that the logarithm of your data follows a normal distribution. The probability density function of the LN curve is given by, where are the mean and standard deviation of the logarithm of distance.

People are often incoherent: their probabilities don't add to 100%. We get an 18% gain in accuracy if we coherentize their estimates. But we get a 30% gain in accuracy if we also assign more weight to coherent estimates.

What implications does this have for making subjective probability maps?

The following figure is from a recent paper I co-authored*:

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages. (The unfortunately abbreviated 'BS' means 'Brier Score'. Lower is better, with 0 being perfect.)

What implications does it have for making subjective "consensus" probability maps at the start of a search?

At Mason we're collaborating with Paul to test a Watershed-Distance model developed by his research group. Based on 58 tests run so far by Elena Sava on MapScore, this simple model scores 0.55. Not bad for a model that doesn't yet discriminate by category (or any other feature). Elena just finished a multivariate model combining Watersheds with the more usual crows'-flight distance, and we will begin testing that soon.

The SARBayesMapScore server has been running for a month now at http://mapscore.sarbayes.org. It's a portal for scoring probability maps, so researchers like us can measure how well we are doing, and see which approaches work best for which situations. Take a look. (And if you have a model, register and start testing it!)

Lin & Goodrich at Brigham Young are working on Bayesian motion models for generating probability maps. They have an interesting model, but need GPS tracks to train it. It's a nice complement to our approach, and it will be interesting to see how they compare.

~Originally a very cool review published in the first half of 2010. The review led to phone calls and a very productive collaboration on MapScore and other work.